Volume 17, Issue 2 (Journal of Control, V.17, N.2 Summer 2023)
JoC 2023, 17(2): 81-111 |
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Khaki-Sedigh A, Nasrollahi S. A review of data-driven control systems design: concepts and methods. JoC 2023; 17 (2) :81-111
URL: http://joc.kntu.ac.ir/article-1-1007-en.html
URL: http://joc.kntu.ac.ir/article-1-1007-en.html
1- Department of Electrical Engineering, K. N. Toosi University of Technology
2- K. N. Toosi University of Technology
2- K. N. Toosi University of Technology
Abstract: (1281 Views)
Over the past two decades, an increasing number of engineers and researchers in the field of control engineering have shifted their focus towards data-driven methods for system analysis and design. The defining characteristic of these data-driven approaches is their departure from conventional models and their associated assumptions. Instead, these methods harness the wealth of readily available, cost-effective, and reliable data derived from real, complex, and complex adaptive systems. Their primary objective is to facilitate system analysis and control solely through measured data, without relying on explicit or implicit model utilization. In this article, we commence by presenting an overview of the design principles embedded in model-based control systems, with a specific emphasis on adaptive and robust control system design methods. Subsequently, we delve into the fundamental principles of data-driven control system design. To comprehensively examine these data-driven methods, we categorize them into two groups: those grounded in machine learning and soft computing, and those based on traditional control systems analysis and design methods, often referred to as classical methods. Within this article, we initiate with a concise review of machine learning and soft computing-based methods before delving into a more comprehensive exploration of classical methods in this field.
Keywords: data-driven control, system design, classical methods, machine learning, model-based control
Type of Article: Research paper |
Subject:
New approaches in control engineering
Received: 2023/07/30 | Accepted: 2023/09/11 | ePublished ahead of print: 2023/09/16 | Published: 2023/09/21
Received: 2023/07/30 | Accepted: 2023/09/11 | ePublished ahead of print: 2023/09/16 | Published: 2023/09/21
References
1. [1] A. M. Annaswamy, K. H. Johansson, and G. J. Pappas, "Control for societal-scale challenges: Road map 2030," IEEE Control Systems Society, 2023.
2. [2] A. Khaki-Sedigh, An Introduction to Data-Driven Control Systems, 1st ed. Wiley-IEEE Press, 2024.. [DOI:10.1002/9781394196432]
3. [3] Z.-S. Hou and Z. Wang, "From model-based control to data-driven control: Survey, classification and perspective," Information Sciences, vol. 235, pp. 3-35, 2013. [DOI:10.1016/j.ins.2012.07.014]
4. [4] J. C. Maxwell, "I. On governors," Proceedings of the Royal Society of London, no. 16, pp. 270-283, 1868. [DOI:10.1098/rspl.1867.0055]
5. [5] C.-G. Kang, "Origin of Stability Analysis:" On Governors" by JC Maxwell [Historical Perspectives]," IEEE Control Systems Magazine, vol. 36, no. 5, pp. 77-88, 2016. [DOI:10.1109/MCS.2016.2584358]
6. [6] J. G. Ziegler and N. B. Nichols, "Optimum settings for automatic controllers," Transactions of the American society of mechanical engineers, vol. 64, no. 8, pp. 759-765, 1942. [DOI:10.1115/1.4019264]
7. [7] K. J. Astrom, "PID controllers: theory, design, and tuning," The International Society of Measurement and Control, 1995.
8. [8] M. Stefanovic and M. G. Safonov, Safe adaptive control: Data-driven stability analysis and robust synthesis. Springer, 2011.
9. [9] J.-W. Huang and J.-W. Gao, "How could data integrate with control? A review on data-based control strategy," International Journal of Dynamics and Control, vol. 8, no. 4, pp. 1189-1199, 2020. [DOI:10.1007/s40435-020-00688-x]
10. [10] M. G. Safonov and T.-C. Tsao, "The unfalsified control concept: A direct path from experiment to controller," in Feedback Control, Nonlinear Systems, and Complexity, 1995: Springer, pp. 196-214. [DOI:10.1007/BFb0027678]
11. [11] B. D. Anderson and A. Dehghani, "Challenges of adaptive control-past, permanent and future," Annual reviews in control, vol. 32, no. 2, pp. 123-135, 2008. [DOI:10.1016/j.arcontrol.2008.06.001]
12. [12] م. علیمحمدی, "طراحی سیستم های کنترل چندمتغیره داده راند بر پایه نظریه کنترل تطبیقی ابطال ناپذیر," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1401.
13. [13] م. مقدسی, "بهبود عملکرد کنترل تطبیقی ابطال ناپذیر در سیستم های تک ورودی تک خروجی," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1402.
14. [14] م. سلیمانی, "بهبود عملکرد کنترل تطبیقی ابطال ناپذیر در سیستم های چندورودی چندخروجی," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1402.
15. [15] M. C. Campi and S. M. Savaresi, "Direct nonlinear control design: The virtual reference feedback tuning (VRFT) approach," IEEE Transactions on Automatic Control, vol. 51, no. 1, pp. 14-27, 2006. [DOI:10.1109/TAC.2005.861689]
16. [16] M. C. Campi, A. Lecchini, and S. M. Savaresi, "Virtual reference feedback tuning: a direct method for the design of feedback controllers," Automatica, vol. 38, no. 8, pp. 1337-1346, 2002. [DOI:10.1016/S0005-1098(02)00032-8]
17. [17] ف. همتی, "طراحی کنترل کننده دو درجه آزادی برای سیستم های ناکمینه فاز با روش تنظیم فیدبک مرجع مجازی," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1401.
18. [18] م. جدی, "تنظیم بازخورد مرجع مجازی به همراه انتخاب مدل مرجع داده رانده¬ی بهینه برای طراحی کنترلگر PID در سامانه های چندورودی/چندخروجی," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1402.
19. [19] S. Yahagi and I. Kajiwara, "Direct tuning method of gain‐scheduled controllers with the sparse polynomials function," Asian journal of Control, vol. 24, no. 5, pp. 2111-2126, 2022. [DOI:10.1002/asjc.2657]
20. [20] J. C. Spall, "An overview of the simultaneous perturbation method for efficient optimization," Johns Hopkins apl technical digest, vol. 19, no. 4, pp. 482-492, 1998.
21. [21] J. C. Spall, "Multivariate stochastic approximation using a simultaneous perturbation gradient approximation," IEEE transactions on automatic control, vol. 37, no. 3, pp. 332-341, 1992. [DOI:10.1109/9.119632]
22. [22] J. C. Spall, "Implementation of the simultaneous perturbation algorithm for stochastic optimization," IEEE Transactions on aerospace and electronic systems, vol. 34, no. 3, pp. 817-823, 1998. [DOI:10.1109/7.705889]
23. [23] M. Nouri Manzar and A. Khaki‐Sedigh, "Online data‐driven control of variable speed wind turbines using the simultaneous perturbation stochastic approximation approach," Optimal Control Applications and Methods, vol. 44, no. 4, pp. 2082-2092, 2023. [DOI:10.1002/oca.2966]
24. [24] A. Yonezawa, H. Yonezawa, and I. Kajiwara, "Efficient parameter tuning to enhance practicability of a model-free vibration controller based on a virtual controlled object," Mechanical Systems and Signal Processing, vol. 200, p. 110526, 2023. [DOI:10.1016/j.ymssp.2023.110526]
25. [25] I. Markovsky, J. C. Willems, S. Van Huffel, and B. De Moor, Exact and approximate modeling of linear systems: A behavioral approach. SIAM, 2006. [DOI:10.1137/1.9780898718263]
26. [26] H. J. van Waarde, C. De Persis, M. K. Camlibel, and P. Tesi, "Willems' fundamental lemma for state-space systems and its extension to multiple datasets," IEEE Control Systems Letters, vol. 4, no. 3, pp. 602-607, 2020. [DOI:10.1109/LCSYS.2020.2986991]
27. [27] J. Coulson, J. Lygeros, and F. Dörfler, "Data-enabled predictive control: In the shallows of the DeePC," in 2019 18th European Control Conference (ECC), 2019: IEEE, pp. 307-312. [DOI:10.23919/ECC.2019.8795639]
28. [28] R. Ou, G. Pan, and T. Faulwasser, "Data-driven multiple shooting for stochastic optimal control," IEEE Control Systems Letters, vol. 7, pp. 313-318, 2022. [DOI:10.1109/LCSYS.2022.3185841]
29. [29] M. Korda and I. Mezić, "Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control," Automatica, vol. 93, pp. 149-160, 2018. [DOI:10.1016/j.automatica.2018.03.046]
30. [30] S. L. Brunton, M. Budišić, E. Kaiser, and J. N. Kutz, "Modern Koopman theory for dynamical systems," arXiv preprint arXiv:2102.12086, 2021. [DOI:10.1137/21M1401243]
31. [31] I. Mezić and A. Banaszuk, "Comparison of systems with complex behavior," Physica D: Nonlinear Phenomena, vol. 197, no. 1-2, pp. 101-133, 2004. [DOI:10.1016/j.physd.2004.06.015]
32. [32] P. J. Schmid, "Dynamic mode decomposition of numerical and experimental data," Journal of fluid mechanics, vol. 656, pp. 5-28, 2010. [DOI:10.1017/S0022112010001217]
33. [33] A. Mauroy, Y. Susuki, and I. Mezić, Koopman operator in systems and control. Springer, 2020. [DOI:10.1007/978-3-030-35713-9]
34. [34] B. O. Koopman, "Hamiltonian systems and transformation in Hilbert space," Proceedings of the National Academy of Sciences, vol. 17, no. 5, pp. 315-318, 1931. [DOI:10.1073/pnas.17.5.315]
35. [35] T. Gholaminejad and A. Khaki‐Sedigh, "Stable data‐driven Koopman predictive control: Concentrated solar collector field case study," IET Control Theory & Applications, vol. 17, no. 9, pp. 1116-1131, 2023. [DOI:10.1049/cth2.12442]
36. [36] T. Gholaminejad and A. Khaki-Sedigh, "Stable deep Koopman model predictive control for solar parabolic-trough collector field," Renewable Energy, vol. 198, pp. 492-504, 2022. [DOI:10.1016/j.renene.2022.08.012]
37. [37] ط. غلامی¬نژاد, "طراحی کنترل پیش بین مبتنی بر اپراتور کوپمن برای نیروگاه های خورشیدی متمرکز سهموی," رساله دکتری, دانشگاه خواجه نصیرالدین طوسی, 1402.
38. [38] V. Toro, D. Tellez-Castro, E. Mojica-Nava, and N. Rakoto-Ravalontsalama, "Data-driven distributed voltage control for microgrids: A Koopman-based approach," International Journal of Electrical Power & Energy Systems, vol. 145, p. 108636, 2023. [DOI:10.1016/j.ijepes.2022.108636]
39. [39] Z. Hou and S. Jin, Model free adaptive control: theory and applications. CRC press, 2013. [DOI:10.1201/b15752]
40. [40] S. Xiong and Z. Hou, "Model-free adaptive control for unknown MIMO nonaffine nonlinear discrete-time systems with experimental validation," IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 4, pp. 1727-1739, 2020. [DOI:10.1109/TNNLS.2020.3043711]
41. [41] س. نصراللهی, "طراحی داده رانده برای کنترل ردیابی مسیر در سیستم های زیرتحریک," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1400.
42. [42] Y. Zhang and J. Song, "Nonlinear leader-following MASs control: a data-driven adaptive sliding mode approach with prescribed performance," Nonlinear Dynamics, vol. 108, no. 1, pp. 349-361, 2022. [DOI:10.1007/s11071-022-07218-8]
43. [43] ا. سلیمانی, "کنترل کواد روتور با روش های داده رانده," پایان نامه کارشناسی ارشد, دانشگاه خواجه نصیرالدین طوسی, 1402.
44. [44] K. Magkoutas, P. Arm, M. Meboldt, and M. Schmid Daners, "Physiologic data-driven iterative learning control for left ventricular assist devices," Frontiers in Cardiovascular Medicine, vol. 9, p. 922387, 2022. [DOI:10.3389/fcvm.2022.922387]
45. [45] A. Karimi, L. Mišković, and D. Bonvin, "Convergence analysis of an iterative correlation-based controller tuning method," IFAC Proceedings Volumes, vol. 35, no. 1, pp. 413-418, 2002. [DOI:10.3182/20020721-6-ES-1901.00150]
46. [46] A. Karimi, L. Mišković, and D. Bonvin, "Iterative correlation-based controller tuning with application to a magnetic suspension system," Control Engineering Practice, vol. 11, no. 9, pp. 1069-1078, 2003. [DOI:10.1016/S0967-0661(02)00191-0]
47. [47] L. Mišković, A. Karimi, D. Bonvin, and M. Gevers, "Correlation-based tuning of decoupling multivariable controllers," Automatica, vol. 43, no. 9, pp. 1481-1494, 2007. [DOI:10.1016/j.automatica.2007.02.006]
48. [48] A. S. Bazanella, L. Campestrini, and D. Eckhard, Data-driven controller design: the H2 approach. Springer Science & Business Media, 2011.
49. [49] R.-E. Precup, R.-C. Roman, and A. Safaei, Data-driven model-free controllers. CRC Press, 2021. [DOI:10.1201/9781003143444]
50. [50] C. Novara and S. Formentin, Data-Driven Modeling, Filtering and Control: Methods and applications (Control, Robotics and Sensors). The Institution of Engineering and Technology, 2019, p. 304. [DOI:10.1049/PBCE123E]
51. [51] K. Prag, M. Woolway, and T. Celik, "Toward data-driven optimal control: A systematic review of the landscape," IEEE Access, vol. 10, pp. 32190-32212, 2022. [DOI:10.1109/ACCESS.2022.3160709]
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